Modelling systemic COVID-19 impacts in cities

Urban resilience is a multifaceted concept encompassing reactive (returning to the status quo), responsive (adapting to change), and proactive (transforming) dimensions. Cities, with their dense concentrations of people and activities, are highly vulnerable to systemic impacts from hazards like pandemics. The COVID-19 pandemic underscores the need for a complex systems approach to urban resilience due to several factors. First, the intricate interconnections between urban functions (e.g., education, economy, healthcare) create complex cascading effects. Second, the pandemic's long-term impacts are significant and require analysis beyond short-term observations. Third, the experiences and responses to the pandemic varied significantly across cities, highlighting the importance of local context. Finally, a broader set of priorities beyond economic considerations are crucial for effective urban management and planning. Existing approaches, like panarchy, offer conceptual frameworks but lack the practical tools to effectively model these complexities. The Urban Systems Abstraction Hierarchy (USAH) is presented as a solution, capable of handling cross-sectoral interdependencies, modeling short-to-long-term impact translation, incorporating local variations, and encompassing diverse priorities.

The introduction extensively reviews existing literature on urban resilience, highlighting the polysemic nature of the concept and the need for complexity-smart approaches. It cites various definitions and frameworks, including the 100 Resilient Cities (100RC) initiative. The literature review emphasizes the limitations of current research in addressing the interconnectedness of urban functions, the long-term consequences of short-term shocks, the importance of local context, and the need for a broader set of priorities in urban planning and management. The authors discuss the shortcomings of existing approaches like panarchy in providing practical tools for modelling city-level systemic impacts and introduce the USAH as a promising alternative.

The study utilizes the Urban Systems Abstraction Hierarchy (USAH), a five-level hierarchical network model adapted from the abstraction hierarchy method in human factors. The model links City Resources (Level 5), Processes (Level 4), Tasks (Level 3), Outcomes (Level 2), and Purposes (Level 1). The researchers first created a generic UK city USAH model, rigorously validated by subject matter experts, and then adapted it for Edinburgh using open-source software (OSMtidy and AHgen) to identify resources within the city council boundary. The Edinburgh-specific model was then modified 30 times to reflect weekly conditions during the COVID-19 pandemic (March-October 2020). This involved weighting links between Resources and Processes based on 23 quantitative indicators derived from national and regional datasets reflecting economic response, healthcare systems, and containment/closure measures. Three additional links were added to represent pandemic-specific adaptations. Network analysis, specifically weighted eigenvector centrality (EC), was used to assess the relative influence of nodes at each level. Sensitivity analysis and Mann-Whitney tests were conducted to evaluate the robustness of the results and assess the statistical significance of changes compared to the pre-pandemic baseline. Rank changes in node positions were analyzed to identify shifts in systemic priorities.

The analysis revealed significant shifts in the relative importance of different nodes within the USAH throughout the modelled period. Mann-Whitney tests showed that Outcomes were significantly different from the baseline during lockdown, Phase 1, and Phase 2. All Outcomes increased in eigenvector centrality (EC) during the lockdown, peaking then decreasing and approaching but not fully returning to baseline values by Phase 3. However, rank changes indicated re-prioritization of system goals. During lockdown, Outcomes related to health, safety, leadership, and communication increased in rank, while those related to the economy, community, and employment decreased. Similar trends were observed in the Tasks level. During the lockdown phase, Tasks related to health and infrastructure were prioritized, while those related to economy and social activities were de-prioritized. The analysis further revealed that while eigenvector centrality values did not show statistical significant differences between weeks for the Tasks level, individual Task ranks changed substantially throughout the studied period, with the most significant changes occurring between pre-lockdown and lockdown, and between Phase 2 and Phase 3. Tables 2 and 3 highlight tasks experiencing large rank changes which were robust to sensitivity analysis. By the end of the modeled period, many nodes returned to or near their baseline ranks, but some remained significantly different indicating persistent long-term impacts. Sensitivity analysis demonstrated that the USAH approach is robust, with the majority of rank findings showing high confidence.

The findings address the research question by demonstrating how the USAH model effectively captures the systemic impacts of the COVID-19 pandemic in Edinburgh. The significant changes in both EC values and ranks of nodes at different levels illustrate the cascading effects of short-term shocks on long-term outcomes. The study highlights the dynamic interplay between different sectors and priorities within the urban system. The re-prioritization of health and safety over economic concerns during lockdown reflects the immediate response to the crisis, while the gradual return to more balanced priorities in later phases reflects the easing of restrictions and the city's recovery. The sensitivity analysis demonstrates the robustness of the USAH model, and the high confidence of most rank findings validates the identified trends. The results are consistent with wider research on the impact of pandemics on urban systems, particularly the exacerbation of existing inequalities and the importance of strong leadership and governance.

This paper successfully demonstrates the USAH's ability to model systemic impacts of the COVID-19 pandemic, showing how short-term shocks translate into long-term impacts across multiple sectors. The Edinburgh case study highlights the re-prioritization of urban system priorities. The findings suggest a need for holistic recovery strategies that extend beyond immediate economic concerns to address a wide range of resilience outcomes. Future research should explore the USAH's applicability to other cities and hazard types. The model's capability for scenario planning and policy prototyping can inform integrated reactive, responsive, and proactive resilience strategies.

The study focuses on the urban system's functionality rather than individual health impacts. The data used reflects government-imposed measures, and may not fully capture the contributions of community-based initiatives. The analysis is limited to a specific time period and location. While the sensitivity analysis demonstrates the robustness of the method, some uncertainty remains in the precise ranking of certain nodes due to the complexity of the data and the sensitivity analysis approach.